class_docstr: PUBDEV-2980: deeplearning return the field model._model_json['output']['scoring_history'].cell_values
  with different lengths.  Normally, it is length 3.  Sometimes, it is length 2.
  
  
  This class is created to train a deeplearning model with different parameters settings and re-create two
  models with different field length for debugging purposes.
docstr: This function performs all initializations necessary:
  load the data sets and set the training set indices and response column index
docstr_tok: ["This","function","performs","all","initializations","necessary",":","load","the","data","sets","and","set","the","training","set","indices","and","response","column","index"]
code: def setup_data(self):
      """
      This function performs all initializations necessary:
      load the data sets and set the training set indices and response column index
      """
      # preload datasets
      self.training1_data = h2o.import_file(
          path=pyunit_utils.locate(self.training1_filename)
      )
  
      # set data set indices for predictors and response
      self.y_index = self.training1_data.ncol - 1
      self.x_indices = list(range(self.y_index))
  
file_path: h2o-3/h2o-py/dynamic_tests/testdir_algos/deeplearning/pyunit_PUBDEV_2980_deeplearning_badfields_NOPASS.py
imports: import sys
  import os
  from builtins import range
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
label_desc: Use image node_id 2 for calling the Test_PUBDEV_2980_deeplearning obj's underlying member method code with example usage: obj.setup_data() without return types